Výsledky vyhledávání - "Jeon, Jeong In"

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Traditional neural networks (multi-layer perceptrons) have become an important tool in data science due to their success across a wide range of tasks. However, their performance is sometimes unsatisfactory, and they often require a large number of pa

Externí odkaz: http://arxiv.org/abs/2411.09175

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Local Fr\'echet regression with circular predictors

Autor: Im, Chang Jun, Jeon, Jeong Min

Fr\'echet regression extends the principles of linear regression to accommodate responses valued in generic metric spaces. While this approach has primarily focused on exploring relationships between Euclidean predictors and non-Euclidean responses,

Externí odkaz: http://arxiv.org/abs/2408.10118

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Density estimation and regression analysis on S^d in the presence of measurement error

Autor: Jeon, Jeong Min, Van Keilegom, Ingrid

This paper studies density estimation and regression analysis with contaminated data observed on the unit hypersphere S^d. Our methodology and theory are based on harmonic analysis on general S^d. We establish novel nonparametric density and regressi

Externí odkaz: http://arxiv.org/abs/2301.03000

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Additive regression with general imperfect variables

Autor: Jeon, Jeong Min, Van Bever, Germain

In this paper, we study an additive model where the response variable is Hilbert-space-valued and predictors are multivariate Euclidean, and both are possibly imperfectly observed. Considering Hilbert-space-valued responses allows to cover Euclidean,

Externí odkaz: http://arxiv.org/abs/2212.05745

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Factors that affect the technological transition of firms toward the industry 4.0 technologies

Autor: Kim, Seung Hwan, Jeon, Jeong hwan, Aridi, Anwar, Jun, Bogang

This research aims to identify factors that affect the technological transition of firms toward industry 4.0 technologies (I4Ts) focusing on firm capabilities and policy impact using relatedness and complexity measures. For the analysis, a unique dat

Externí odkaz: http://arxiv.org/abs/2209.02239

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